Loading Necessary Packages

Reading in Dataframes

# Community data wide format
cover.wide <- read.csv("../../Data/cover_wide_tradeoffs.csv")
# Site attributes
site.attr <- read.csv("../../Data/comb-by-plot-clim-soil-diversity-02-Aug-2019.csv")

Subsetting datasets to attribute and community matrices

Fitting RRPP models to each site

output_list <- list()
counter <- 1

sitenames = unique(attr.mat$site_code)
sitenames = sitenames[!(sitenames %in% c("doane.us", "pape.de", "sevi.us"))]

# For all unique sites selected
for(sites in sitenames){

  print(paste(counter,":", sites))

  # Subset to a single site
  com.subset <- cover.mat[attr.mat$site_code == sites,]

  # Remove all zero columns
  com.subset <- com.subset[,colSums(com.subset) > 0]

  # Pull out relevant plot attributes for each site
  attr.subset <- data.frame(attr.mat) %>%
    filter(site_code == sites) %>%
    mutate(year_trt = as.factor(year_trt),
           plot = as.factor(plot),
           block = as.factor(block))

  # Check that these two matrices are the same size
  expect_true(nrow(attr.subset) == nrow(com.subset))
  expect_true(nrow(attr.subset) > 0)

  mod_rrpp <- lm.rrpp(com.subset ~ year_trt + trt_K_num + trt_P_num + trt_N_num + block,
                      data = attr.subset,
                      SS.type = "III",
                      iter = num_iter,
                      print.progress = FALSE)

  # Saving ouput
  output = list(siteinfo = attr.subset,
                aovtable = anova(mod_rrpp, effect.type = "F",
                                  error = c("Residuals", "Residuals", "Residuals",
                                            "Residuals", "Residuals")),
                specscores = coef(mod_rrpp))

  output_list[[counter]] = output
  counter = counter + 1
}
storagelist <- list()
for(i in 1:length(output_list)){
  anovatab = bind_cols(site = as.character(unique(output_list[[i]]$siteinfo$site_code)),
            data.frame(matrix(output_list[[i]]$aovtable$table$`Pr(>F)`, nrow = 1)))
  colnames(anovatab)[2:ncol(anovatab)] =   rownames(output_list[[i]]$aovtable$table)
  storagelist[[i]] = anovatab
}
write.csv(x = bind_rows(storagelist),
  "../../Data/tradeoffs_RRPP_Pvals.csv")
storagelist <- list()
for(i in 1:length(output_list)){
  sp_scores = data.frame(output_list[[i]]$specscores[grep("trt_", rownames(output_list[[i]]$specscores)),])
  tomatch = c(4:10)
  # sp_scores = sp_scores[grepl(paste(tomatch, collapse = "|"), rownames(sp_scores)),]
  spectab = bind_cols(site = rep(unique(output_list[[i]]$siteinfo$site_code), nrow(sp_scores)),
                    trt = rownames(sp_scores),
                    sp_scores)
  storagelist[[i]] = spectab %>% mutate(trt = gsub("_num", "", trt))
}
specscores_full = data.frame(bind_rows(storagelist))
specscores_full[is.na(specscores_full)] = 0
write.csv(specscores_full, "../../Data/tradeoffs_specscores.csv")

Calculate dot-product of pairwise correlations

Given these estimated parameters, we can calculate the correlation between species responses to different pairs of nutrient treatments through the dot-product of the fitted vectors between treatments within a site.

After calculating this dot-product, the distributions of each set correlation scores can be seen in histograms below:

# Normalization function -- squared sums of vector = 1
scalar1 <- function(x) {x / sqrt(sum(x^2))}

# Defining dot-product function
dotprod <- function(x, normalize = TRUE){

  # First, select the appropriate treatment and remove columns that aren't plant response
  N_vec <- x %>% filter(trt == "trt_N") %>% select(-trt, -site)
  P_vec <- x %>% filter(trt == "trt_P") %>% select(-trt, -site)
  K_vec <- x %>% filter(trt == "trt_K") %>% select(-trt, -site)

  N_change <- sqrt(sum(N_vec^2))
  P_change <- sqrt(sum(P_vec^2))
  K_change <- sqrt(sum(K_vec^2))

  # If normalizing, run the normalize function on each vector
  if(normalize == TRUE){
    N_vec <- scalar1(N_vec)
    P_vec <- scalar1(P_vec)
    K_vec <- scalar1(K_vec)
  }

  # Generate output dataframe of dot products (pracma::dot function)
  output = data.frame(NP = pracma::dot(as.numeric(N_vec), as.numeric(P_vec)),
                      NK = pracma::dot(as.numeric(N_vec), as.numeric(K_vec)),
                      PK = pracma::dot(as.numeric(P_vec), as.numeric(K_vec)),
                      N_change = N_change,
                      P_change = P_change,
                      K_change = K_change)

  return(output)

}

# Run over our dataset
dotoutput = specscores_full %>% group_by(site) %>%
  do(dotprod(.))

# Load permANOVA p-value output
site_pvals = read.csv("../../Data/tradeoffs_RRPP_Pvals.csv", header = TRUE, stringsAsFactors = FALSE)

# Join all together (sites, treatments, dot-product pairs, perMANOVA p-values)
dot_full = left_join(site_pvals %>% select(-Total, -Residuals), dotoutput) %>%
  select(-X) %>%
  rename("trt_K" = "trt_K_num", "trt_P" =  "trt_P_num", "trt_N" = "trt_N_num")

# Write final data product
write.csv(x = dot_full, "../../Data/dot_full.csv")

Plotting correlation between different vectors of change

dot_full <- read.csv("../../Data/dot_full.csv")
p1 = dot_full %>% # filter(trt_N < .05 & trt_P < .05) %>%
  ggplot(aes(x = NP,
             fill = trt_N < 0.05 & trt_P < .05)) +
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_N < .05 & trt_P < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(!(trt_N < .05 & trt_P < .05))) +
  xlim(-1, 1) +
  ggtitle("N - P Correlation") +
  guides(fill = FALSE) +
  scale_y_continuous(limits = c(-4,4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2))) +
  geom_text(aes(x = -.25, y = 3.5), label = "Both vectors significant (p < 0.05)", color = "turquoise4") +
  geom_text(aes(x = -.25, y = -3.5), label = "=< 1 vectors significant (p < 0.05)", color = "tomato2")
p2 = dot_full %>% # filter(trt_N < .05 & trt_K < .05) %>%
  ggplot(aes(x = NK,
             fill = trt_N < 0.05 & trt_K < .05)) +
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_K < .05 & trt_N < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25, 
                 data = dot_full %>% filter(!(trt_K < .05 & trt_N < .05))) +  
  xlim(-1, 1) +
  ggtitle("N - K Correlation")+
  guides(fill = FALSE) +
  scale_y_continuous(limits = c(-4,4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2)))
p3 = dot_full %>% # filter(trt_K < .05 & trt_P < .05) %>%
  ggplot(aes(x = PK,
             fill = trt_K < 0.05 & trt_P < .05))+
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_K < .05 & trt_P < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(!(trt_K < .05 & trt_P < .05))) +
  xlim(-1, 1) +
  ggtitle("P - K Correlation")+
  guides(fill = FALSE)+
  scale_y_continuous(limits = c(-4, 4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2)))
grid.arrange(p1,p2,p3,
             top = textGrob("Site Response Correlations",gp=gpar(fontsize=20,font=3)),
             nrow = 3)

Visualizing Example Communities

rdaFit = function(site){
  com.subset <- data.frame(cover.mat)[attr.mat$site_code == site,]
  
  # Remove all zero columns
  com.subset = com.subset[,colSums(com.subset) > 0]
  
  # Pull out relevant plot attributes for each site
  attr.subset <- data.frame(attr.mat) %>% filter(site_code == site)
  
  # Check that these two matrices are the same size
  expect_true(nrow(attr.subset) == nrow(com.subset))
  
  # Generate and return an NMDS figure
  mod_out <- vegan::metaMDS(com.subset, distance = "euclidean", autotransform = FALSE, try = 50)
  env <- envfit(mod_out, attr.subset %>% select(block, trt_N_num, trt_P_num, trt_K_num) %>% mutate(block = as.factor(block)))

  # Generating GGplot
  ggplot() + 
    geom_point(aes(x = MDS1, y = MDS2), pch = 21, fill = "white", alpha = .5, size = 2,
               data = data.frame(mod_out$points) * attr(mod_out$points, "internalscaling")) +
    geom_segment(aes(x = 0, y = 0, xend = NMDS1, yend = NMDS2), data = data.frame(env$vectors$arrows), size = 2, 
                 arrow = arrow(length = unit(0.5, "cm"), type = "closed")) +
     geom_point(aes(x = NMDS1, y = NMDS2), data = data.frame(env$vectors$arrows), alpha = 0, size = 6) + 
    ggrepel::geom_text_repel(aes(x = NMDS1, y = NMDS2), 
              data = data.frame(env$vectors$arrows), 
              label = gsub("_num", "", rownames(data.frame(env$vectors$arrows))),
              size = 6) +
    ggtitle(site)
}

rdaFit("rook.uk"); ggsave("../../Figures/rook_nmds.pdf", height = 5, width = 5)
rdaFit("bnch.us"); ggsave("../../Figures/bnch_nmds.pdf", height = 5, width = 5)

Summarising communities by functional group abundance

---
title: "R Notebook"
output: html_notebook
---

### Loading Necessary Packages

```{r, message = FALSE, echo = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE,fig.width = 5, fig.height = 3.5)

# Loading necessary packages
library(tidyverse) # Data manipulation
library(vegan) # General ecology functions
library(data.table) # Fast reading of data tables
library(testthat) # Unit testing
library(fastDummies) # Fast generation of dummy variables
# library(dtplyr) #Dplyr for data.table
library(RRPP) # RRPP for multivariate model fittinginsta
library(gridExtra)
library(grid)
```

### Reading in Dataframes

```{r}
# Community data wide format
cover.wide <- read.csv("../../Data/cover_wide_tradeoffs.csv")

# Site attributes
site.attr <- read.csv("../../Data/comb-by-plot-clim-soil-diversity-02-Aug-2019.csv")

# Analysis parameters
min_yr_trt <- 5 # Number of years minimum to be included in analysis

# Number of iterations in RRPP
num_iter <- 9999

```

### Subsetting datasets to attribute and community matrices

```{r}
# Checking that all cover values are numeric
cols <- c(colnames(cover.wide)[-c(1:6)])
cover.wide[-c(1:6)] <- apply(cover.wide[-c(1:6)], MARGIN = 2, FUN = as.numeric)

# Separating dataframe into cover and attribute
attr.mat = cover.wide[,1:7] %>% column_to_rownames(var="X")
cover.vals = data.frame(cover.wide[,-c(2:7)]) %>% column_to_rownames(var="X")

# Should we normalize?
## Normalizing might be standard if we're dealing in proportions
## However, we have a minimum cover threshold, usually about .5
## normalizing in this case might cause problems if we have total cover increasing
## this can cause low-abundance species to become less abundant, even if they are
## the same absolute abundance in both cases

# cover.mat = round(vegan::decostand(cover.vals, method = "total"), 3)
cover.mat = cover.vals

# Creating new dummy variables that refer to numeric treatment effects
attr.mat = dummy_columns(attr.mat, select_columns = "trt",
                           remove_first_dummy = TRUE) %>%
  replace_na(list(trt_K = 0, trt_N = 0, trt_P = 0)) %>%
  mutate(trt_K_num =log(as.numeric(trt_K * year_trt) + 1),
         trt_P_num =log(as.numeric(trt_P * year_trt) + 1),
         trt_N_num =log(as.numeric(trt_N * year_trt) + 1),
         year_trt = log(as.numeric(year_trt) + 1))

# Setting treatment effects to zero for the first year of treatment
attr.mat$trt_K[attr.mat$year_trt == 0] = 0
attr.mat$trt_P[attr.mat$year_trt == 0] = 0
attr.mat$trt_N[attr.mat$year_trt == 0] = 0


# Creating dataset of sites used in the analysis
sites_df <- left_join(attr.mat %>% select(site_code, plot, year), site.attr, by = c("site_code", "plot", "year")) %>% 
  group_by(site_code) %>%
  summarise(site_name = unique(site_name),
            continent = unique(continent),
            country = unique(country),
            n_years = length(unique(year)),
            n_obs = n(),
            first_nutrient_year = unique(first_nutrient_year)) %>%
  filter(!(site_code%in% c("doane.us", "pape.de", "sevi.us")))

write.csv("../../Data/tradeoffs_selectedsites.csv",
          x = sites_df)

# Cleaning workspace
rm(cover.wide, cover.vals)
            
write.csv("../../Data/tradeoffs_comm.csv",
          x = cover.mat,
          row.names = FALSE)

write.csv("../../Data/tradeoffs_attr.csv",
          x = attr.mat,
          row.names = FALSE)
```

### Fitting RRPP models to each site

```{r}
output_list <- list()
counter <- 1

sitenames = unique(attr.mat$site_code)
sitenames = sitenames[!(sitenames %in% c("doane.us", "pape.de", "sevi.us"))]

# For all unique sites selected
for(sites in sitenames){

  print(paste(counter,":", sites))

  # Subset to a single site
  com.subset <- cover.mat[attr.mat$site_code == sites,]

  # Remove all zero columns
  com.subset <- com.subset[,colSums(com.subset) > 0]

  # Pull out relevant plot attributes for each site
  attr.subset <- data.frame(attr.mat) %>%
    filter(site_code == sites) %>%
    mutate(year_trt = as.factor(year_trt),
           plot = as.factor(plot),
           block = as.factor(block))

  # Check that these two matrices are the same size
  expect_true(nrow(attr.subset) == nrow(com.subset))
  expect_true(nrow(attr.subset) > 0)

  mod_rrpp <- lm.rrpp(com.subset ~ year_trt + trt_K_num + trt_P_num + trt_N_num + block,
                      data = attr.subset,
                      SS.type = "III",
                      iter = num_iter,
                      print.progress = FALSE)

  # Saving ouput
  output = list(siteinfo = attr.subset,
                aovtable = anova(mod_rrpp, effect.type = "F",
                                  error = c("Residuals", "Residuals", "Residuals",
                                            "Residuals", "Residuals")),
                specscores = coef(mod_rrpp))

  output_list[[counter]] = output
  counter = counter + 1
}
```

```{r}
storagelist <- list()

for(i in 1:length(output_list)){

  anovatab = bind_cols(site = as.character(unique(output_list[[i]]$siteinfo$site_code)),
            data.frame(matrix(output_list[[i]]$aovtable$table$`Pr(>F)`, nrow = 1)))

  colnames(anovatab)[2:ncol(anovatab)] =   rownames(output_list[[i]]$aovtable$table)

  storagelist[[i]] = anovatab

}

write.csv(x = bind_rows(storagelist),
  "../../Data/tradeoffs_RRPP_Pvals.csv")

storagelist <- list()

for(i in 1:length(output_list)){
  sp_scores = data.frame(output_list[[i]]$specscores[grep("trt_", rownames(output_list[[i]]$specscores)),])

  tomatch = c(4:10)
  # sp_scores = sp_scores[grepl(paste(tomatch, collapse = "|"), rownames(sp_scores)),]

  spectab = bind_cols(site = rep(unique(output_list[[i]]$siteinfo$site_code), nrow(sp_scores)),
                    trt = rownames(sp_scores),
                    sp_scores)

  storagelist[[i]] = spectab %>% mutate(trt = gsub("_num", "", trt))
}

specscores_full = data.frame(bind_rows(storagelist))
specscores_full[is.na(specscores_full)] = 0
write.csv(specscores_full, "../../Data/tradeoffs_specscores.csv")
```

### Calculate dot-product of pairwise correlations

Given these estimated parameters, we can calculate the correlation between species responses
to different pairs of nutrient treatments through the dot-product of the fitted
vectors between treatments within a site.

After calculating this dot-product, the distributions of each set correlation scores can be seen in histograms below:

* Values close to zero indicate that species responses to two treatments within a site are not correlated with one another.

* Values close to one or negative one indicate that species responses to two treatments within a site are highly positively or negatively correlated.

```{r}
# Normalization function -- squared sums of vector = 1
scalar1 <- function(x) {x / sqrt(sum(x^2))}

# Defining dot-product function
dotprod <- function(x, normalize = TRUE){

  # First, select the appropriate treatment and remove columns that aren't plant response
  N_vec <- x %>% filter(trt == "trt_N") %>% select(-trt, -site)
  P_vec <- x %>% filter(trt == "trt_P") %>% select(-trt, -site)
  K_vec <- x %>% filter(trt == "trt_K") %>% select(-trt, -site)

  N_change <- sqrt(sum(N_vec^2))
  P_change <- sqrt(sum(P_vec^2))
  K_change <- sqrt(sum(K_vec^2))

  # If normalizing, run the normalize function on each vector
  if(normalize == TRUE){
    N_vec <- scalar1(N_vec)
    P_vec <- scalar1(P_vec)
    K_vec <- scalar1(K_vec)
  }

  # Generate output dataframe of dot products (pracma::dot function)
  output = data.frame(NP = pracma::dot(as.numeric(N_vec), as.numeric(P_vec)),
                      NK = pracma::dot(as.numeric(N_vec), as.numeric(K_vec)),
                      PK = pracma::dot(as.numeric(P_vec), as.numeric(K_vec)),
                      N_change = N_change,
                      P_change = P_change,
                      K_change = K_change)

  return(output)

}

# Run over our dataset
dotoutput = specscores_full %>% group_by(site) %>%
  do(dotprod(.))

# Load permANOVA p-value output
site_pvals = read.csv("../../Data/tradeoffs_RRPP_Pvals.csv", header = TRUE, stringsAsFactors = FALSE)

# Join all together (sites, treatments, dot-product pairs, perMANOVA p-values)
dot_full = left_join(site_pvals %>% select(-Total, -Residuals), dotoutput) %>%
  select(-X) %>%
  rename("trt_K" = "trt_K_num", "trt_P" =  "trt_P_num", "trt_N" = "trt_N_num")

# Write final data product
write.csv(x = dot_full, "../../Data/dot_full.csv")
```


### Plotting correlation between different vectors of change

```{r, fig.height = 8, fig.width = 4}
dot_full <- read.csv("../../Data/dot_full.csv")

p1 = dot_full %>% # filter(trt_N < .05 & trt_P < .05) %>%
  ggplot(aes(x = NP,
             fill = trt_N < 0.05 & trt_P < .05)) +
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_N < .05 & trt_P < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(!(trt_N < .05 & trt_P < .05))) +
  xlim(-1, 1) +
  ggtitle("N - P Correlation") +
  guides(fill = FALSE) +
  scale_y_continuous(limits = c(-4,4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2))) +
  geom_text(aes(x = -.25, y = 3.5), label = "Both vectors significant (p < 0.05)", color = "turquoise4") +
  geom_text(aes(x = -.25, y = -3.5), label = "=< 1 vectors significant (p < 0.05)", color = "tomato2")

p2 = dot_full %>% # filter(trt_N < .05 & trt_K < .05) %>%
  ggplot(aes(x = NK,
             fill = trt_N < 0.05 & trt_K < .05)) +
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_K < .05 & trt_N < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25, 
                 data = dot_full %>% filter(!(trt_K < .05 & trt_N < .05))) +  
  xlim(-1, 1) +
  ggtitle("N - K Correlation")+
  guides(fill = FALSE) +
  scale_y_continuous(limits = c(-4,4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2)))

p3 = dot_full %>% # filter(trt_K < .05 & trt_P < .05) %>%
  ggplot(aes(x = PK,
             fill = trt_K < 0.05 & trt_P < .05))+
  geom_histogram(aes(y = ..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(trt_K < .05 & trt_P < .05)) +
  geom_histogram(aes(y = -..count..), color = "black", alpha = .5, position = "identity", bins = 25,
                 data = dot_full %>% filter(!(trt_K < .05 & trt_P < .05))) +
  xlim(-1, 1) +
  ggtitle("P - K Correlation")+
  guides(fill = FALSE)+
  scale_y_continuous(limits = c(-4, 4), breaks = seq(-6, 6, by = 2), labels = abs( seq(-6, 6, by = 2)))

grid.arrange(p1,p2,p3,
             top = textGrob("Site Response Correlations",gp=gpar(fontsize=20,font=3)),
             nrow = 3)
```

```{r}
sigtally <- site_pvals %>% select(trt_N_num, trt_P_num, trt_K_num) %>%
  mutate_all(function(x) x < 0.05) %>%
  mutate(NP = trt_N_num == 1 & trt_P_num == 1,
         NK = trt_N_num == 1 & trt_K_num == 1,
         PK = trt_P_num == 1 & trt_K_num == 1,
         NPK = trt_N_num == 1 & trt_P_num == 1 & trt_K_num == 1)  %>%
  mutate_all(as.numeric) %>%
  rename("K" = "trt_K_num",
         "N" = "trt_N_num",
         "P" = "trt_P_num") %>%
  summarise_all(sum) %>%
  gather()

sigtally$key = factor(sigtally$key, levels = sigtally$key)

sigtally %>% ggplot(aes(x = key,
                        y = value / nrow(site_pvals),
                        fill = key)) +
  geom_bar(aes(y = 1), stat = "identity", fill = "white", color = "black") +
  geom_bar(stat = "identity", color = "black") +
  scale_fill_manual(values = c("cadetblue1", "cadetblue3", "cadetblue4", "gold", "gold3", "gold4", "brown3")) +
  ylim(0, 1) +
  ylab("Proportion of Sites where P < 0.05") +
  xlab("Coefficients")
```

### Visualizing Example Communities

```{r, height = 4, width = 3}
rdaFit = function(site){
  com.subset <- data.frame(cover.mat)[attr.mat$site_code == site,]
  
  # Remove all zero columns
  com.subset = com.subset[,colSums(com.subset) > 0]
  
  # Pull out relevant plot attributes for each site
  attr.subset <- data.frame(attr.mat) %>% filter(site_code == site)
  
  # Check that these two matrices are the same size
  expect_true(nrow(attr.subset) == nrow(com.subset))
  
  # Generate and return an NMDS figure
  mod_out <- vegan::metaMDS(com.subset, distance = "euclidean", autotransform = FALSE, try = 50)
  env <- envfit(mod_out, attr.subset %>% select(block, trt_N_num, trt_P_num, trt_K_num) %>% mutate(block = as.factor(block)))

  # Generating GGplot
  ggplot() + 
    geom_point(aes(x = MDS1, y = MDS2), pch = 21, fill = "white", alpha = .5, size = 2,
               data = data.frame(mod_out$points) * attr(mod_out$points, "internalscaling")) +
    geom_segment(aes(x = 0, y = 0, xend = NMDS1, yend = NMDS2), data = data.frame(env$vectors$arrows), size = 2, 
                 arrow = arrow(length = unit(0.5, "cm"), type = "closed")) +
     geom_point(aes(x = NMDS1, y = NMDS2), data = data.frame(env$vectors$arrows), alpha = 0, size = 6) + 
    ggrepel::geom_text_repel(aes(x = NMDS1, y = NMDS2), 
              data = data.frame(env$vectors$arrows), 
              label = gsub("_num", "", rownames(data.frame(env$vectors$arrows))),
              size = 6) +
    ggtitle(site)
}

rdaFit("rook.uk"); ggsave("../../Figures/rook_nmds.pdf", height = 5, width = 5)
rdaFit("bnch.us"); ggsave("../../Figures/bnch_nmds.pdf", height = 5, width = 5)
```

### Summarising communities by functional group abundance

```{r}

```

